diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb
index 2dca19d0b..fa8db03af 100644
--- a/02_activities/assignments/assignment_1.ipynb
+++ b/02_activities/assignments/assignment_1.ipynb
@@ -56,13 +56,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
+    "import numpy as np\n",
+    "\n",
     "# For testing purposes, we will write our code in the function\n",
     "def anagram_checker(word_a, word_b):\n",
-    "  # Your code here\n",
+    "  # find the unique characters in each word and their counts\n",
+    "  chars1, counts = np.unique(np.array(list(word_a.lower()), dtype='U1'), return_counts=True)\n",
+    "  chars2, counts2 = np.unique(np.array(list(word_b.lower()), dtype='U1'), return_counts=True)\n",
+    "  # create dictionaries of the characters and their counts for each word\n",
+    "  dict1 = dict(zip(chars1, counts))\n",
+    "  dict2 = dict(zip(chars2, counts2))\n",
+    "  # return whether the two dictionaries are equal (i.e. the words are anagrams)\n",
+    "  return dict1 == dict2\n",
     "\n",
     "# Run your code to check using the words below:\n",
     "anagram_checker(\"Silent\", \"listen\")"
@@ -70,18 +90,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "anagram_checker(\"Silent\", \"Night\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "anagram_checker(\"night\", \"Thing\")"
    ]
@@ -99,10 +141,35 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
+    "#import numpy as np\n",
+    "\n",
     "def anagram_checker(word_a, word_b, is_case_sensitive):\n",
-    "  # Modify your existing code here\n",
+    "  # is we are not considering case sensitivity, convert both words to lowercase\n",
+    "  if not is_case_sensitive:\n",
+    "    word_a = word_a.lower()\n",
+    "    word_b = word_b.lower()\n",
+    "\n",
+    "  # find the unique characters in each word and their counts\n",
+    "  chars1, counts = np.unique(np.array(list(word_a), dtype='U1'), return_counts=True)\n",
+    "  chars2, counts2 = np.unique(np.array(list(word_b), dtype='U1'), return_counts=True)\n",
+    "  # create dictionaries of the characters and their counts for each word\n",
+    "  dict1 = dict(zip(chars1, counts))\n",
+    "  dict2 = dict(zip(chars2, counts2))\n",
+    "  # return whether the two dictionaries are equal (i.e. the words are anagrams)\n",
+    "  return dict1 == dict2\n",
     "\n",
     "# Run your code to check using the words below:\n",
     "anagram_checker(\"Silent\", \"listen\", False) # True"
@@ -110,18 +177,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "anagram_checker(\"Silent\", \"listen\", True) # False"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "anagram_checker(\"Silent\", \"Listen\", True) # False"
    ]
@@ -139,7 +228,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "new-learner",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -153,7 +242,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,